BACKGROUND:Current sequencing technology makes it practical to sequence many samples of a given organism, raising new challenges for the processing and interpretation of large genomics data sets with associated metadata. Traditional computational phylogenetic methods are ideal for studying the evolution of gene/protein families and using those to infer the evolution of an organism, but are less than ideal for the study of the whole organism mainly due to the presence of insertions/deletions/rearrangements. These methods provide the researcher with the ability to group a set of samples into distinct genotypic groups based on sequence similarity, which can then be associated with metadata, such as host information, pathogenicity, and time or location of occurrence. Genotyping is critical to understanding, at a genomic level, the origin and spread of infectious diseases. Increasingly, genotyping is coming into use for disease surveillance activities, as well as for microbial forensics. The classic genotyping approach has been based on phylogenetic analysis, starting with a multiple sequence alignment. Genotypes are then established by expert examination of phylogenetic trees. However, these traditional single-processor methods are suboptimal for rapidly growing sequence datasets being generated by next-generation DNA sequencing machines, because they increase in computational complexity quickly with the number of sequences.RESULTS:Nephele is a suite of tools that uses the complete composition vector algorithm to represent each sequence in the dataset as a vector derived from its constituent k-mers by passing the need for multiple sequence alignment, and affinity propagation clustering to group the sequences into genotypes based on a distance measure over the vectors. Our methods produce results that correlate well with expert-defined clades or genotypes, at a fraction of the computational cost of traditional phylogenetic methods run on traditional hardware. Nephele can use the open-source Hadoop implementation of MapReduce to parallelize execution using multiple compute nodes. We were able to generate a neighbour-joined tree of over 10,000 16S samples in less than 2 hours.CONCLUSIONS:We conclude that using Nephele can substantially decrease the processing time required for generating genotype trees of tens to hundreds of organisms at genome scale sequence coverage.

Knowledge about pathogenesis is increasing dramatically, and most of this information is stored in the scientific literature or in sequence databases. This information can be made more accessible by the use of ontologies or controlled vocabularies. Recently, several ontologies, controlled vocabularies and databases have been developed or adapted for virulence factors and their roles in pathogenesis. Here, we discuss these systems, how they are being used in research and the challenges that remain for developing and applying ontologies for virulence factors.

We have developed a challenge task for the second BioCreAtIvE (Critical Assessment of Information Extraction in Biology) that requires participating systems to provide lists of the EntrezGene (formerly LocusLink) identifiers for all human genes and proteins mentioned in a MEDLINE abstract. We are distributing 281 annotated abstracts and another 5,000 noisily annotated abstracts along with a gene name lexicon to participants. We have performed a series of baseline experiments to better characterize this dataset and form a foundation for participant exploration.

BACKGROUND:Phylogenetic trees are widely used to visualize evolutionary relationships between different organisms or samples of the same organism. There exists a variety of both free and commercial tree visualization software available, but limitations in these programs often require researchers to use multiple programs for analysis, annotation, and the production of publication-ready images.RESULTS:We present TreeViewJ, a Java tool for visualizing, editing and analyzing phylogenetic trees. The software allows researchers to color and change the width of branches that they wish to highlight, and add names to nodes. If collection dates are available for taxa, the software can map them onto a timeline, and sort the tree in ascending or descending date order.CONCLUSION:TreeViewJ is a tool for researchers to visualize, edit, "decorate," and produce publication-ready images of phylogenetic trees. It is open-source, and released under an GPL license, and available at http://treeviewj.sourceforge.net.

Background Our goal in BioCreAtIve has been to assess the state of the art in text mining, with emphasis on applications that reflect real biological applications, e.g., the curation process for model organism databases. This paper summarizes the BioCreAtIvE task 1B, the "Normalized Gene List" task, which was inspired by the gene list supplied for each curated paper in a model organism database. The task was to produce the correct list of unique gene identifiers for the genes and gene products mentioned in sets of abstracts from three model organisms (Yeast, Fly, and Mouse). Results Eight groups fielded systems for three data sets (Yeast, Fly, and Mouse). For Yeast, the top scoring system (out of 15) achieved 0.92 F-measure (harmonic mean of precision and recall); for Mouse and Fly, the task was more difficult, due to larger numbers of genes, more ambiguity in the gene naming conventions (particularly for Fly), and complex gene names (for Mouse). For Fly, the top F-measure was 0.82 out of 11 systems and for Mouse, it was 0.79 out of 16 systems. Conclusion This assessment demonstrates that multiple groups were able to perform a real biological task across a range of organisms. The performance was dependent on the organism, and specifically on the naming conventions associated with each organism. These results hold out promise that the technology can provide partial automation of the curation process in the near future.

Background We prepared and evaluated training and test materials for an assessment of text mining methods in molecular biology. The goal of the assessment was to evaluate the ability of automated systems to generate a list of unique gene identifiers from PubMed abstracts for the three model organisms Fly, Mouse, and Yeast. This paper describes the preparation and evaluation of answer keys for training and testing. These consisted of lists of normalized gene names found in the abstracts, generated by adapting the gene list for the full journal articles found in the model organism databases. For the training dataset, the gene list was pruned automatically to remove gene names not found in the abstract; for the testing dataset, it was further refined by manual annotation by annotators provided with guidelines. A critical step in interpreting the results of an assessment is to evaluate the quality of the data preparation. We did this by careful assessment of interannotator agreement and the use of answer pooling of participant results to improve the quality of the final testing dataset. Results Interannotator analysis on a small dataset showed that our gene lists for Fly and Yeast were good (87% and 91% three-way agreement) but the Mouse gene list had many conflicts (mostly omissions), which resulted in errors (69% interannotator agreement). By comparing and pooling answers from the participant systems, we were able to add an additional check on the test data; this allowed us to find additional errors, especially in Mouse. This led to 1% change in the Yeast and Fly "gold standard" answer keys, but to an 8% change in the mouse answer key. Conclusion We found that clear annotation guidelines are important, along with careful interannotator experiments, to validate the generated gene lists. Also, abstracts alone are a poor resource for identifying genes in paper, containing only a fraction of genes mentioned in the full text (25% for Fly, 36% for Mouse). We found that there are intrinsic differences between the model organism databases related to the number of synonymous terms and also to curation criteria. Finally, we found that answer pooling was much faster and allowed us to identify more conflicting genes than interannotator analysis.

Retrieving information from text has become an important area in bioinformatics, and not too surprisingly, this journal has published more than 30 papers on this topic since the first article published by the journal in 1998. In addition, ISCB (International Society for Computational Biology) (www.iscb.org) has organized special sessions in the ISMB conferences (Intelligent Systems for Molecular Biology, see www.iscb.org/ismb2005) and a specialized interest group (www.pdg.cnb.uam.es/BioLink/) for the last five years. In parallel, major computer science conferences in related areas have begun to include sessions on biology, e.g. the TREC (text retrieval conference) Genomics track (medir.ohsu.edu/ genomics), ICML (International Conference on Machine Learning) and a series of workshops organized in association with the ACL (Association for Computation Linguistics) and Human Language Technology meetings. The requirements of the text mining community are similar to those in other areas of bioinformatics:

BACKGROUND:The biological research literature is a major repository of knowledge. As the amount of literature increases, it will get harder to find the information of interest on a particular topic. There has been an increasing amount of work on text mining this literature, but comparing this work is hard because of a lack of standards for making comparisons. To address this, we worked with colleagues at the Protein Design Group, CNB-CSIC, Madrid to develop BioCreAtIvE (Critical Assessment for Information Extraction in Biology), an open common evaluation of systems on a number of biological text mining tasks. We report here on task 1A, which deals with finding mentions of genes and related entities in text. "Finding mentions" is a basic task, which can be used as a building block for other text mining tasks. The task makes use of data and evaluation software provided by the (US) National Center for Biotechnology Information (NCBI).RESULTS:15 teams took part in task 1A. A number of teams achieved scores over 80% F-measure (balanced precision and recall). The teams that tried to use their task 1A systems to help on other BioCreAtIvE tasks reported mixed results.CONCLUSION:The 80% plus F-measure results are good, but still somewhat lag the best scores achieved in some other domains such as newswire, due in part to the complexity and length of gene names, compared to person or organization names in newswire.

Our research primarily involves the application of natural language processing technology to biomedical literature in support of such applications as semi-automated functional annotation of proteins and genes, and gene name normalization for improved search and retrieval of text information. We have performed studies in the use of existing database resources in these efforts (Morgan, Hirschman et al. 2003) and together with CNB/CSIC-Madrid, we have organized and administered a challenge evaluation, BioCreAtIvE (Valencia, Blaschke et al. 2004), for text mining systems applied to biomedical literature. Our primary experience with ontologies is with GO (The Gene Ontology Consortium 2000), and with some of the specific hierarchical controlled vocabularies. These include the FlyBase Controlled Vocabulary (The FlyBase Consortium 1993) and the TVFac Hierarchy (http://www.tvfac.lanl.gov/right.html); our focus has been on automating the association of small excerpts of text and the underlying entities described (mentioned) in the text with concepts in the ontologies. We focus here on how existing ontologies and related resources can be augmented to aid text-mining and how text mining evaluation techniques can contribute to ontology evolution, by viewing ontologies as annotation guidelines when constructing/populating them.

Biology has now become an information science, and researchers are increasingly dependent on expert-curated biological databases to organize the findings from the published literature. We report here on a series of experiments related to the application of natural language processing to aid in the curation process for FlyBase. We focused on listing the normalized form of genes and gene products discussed in an article. We broke this into two steps: gene mention tagging in text, followed by normalization of gene names. For gene mention tagging, we adopted a statistical approach. To provide training data, we were able to reverse engineer the gene lists from the associated articles and abstracts, to generate text labeled (imperfectly) with gene mentions. We then evaluated the quality of the noisy training data (precision of 78%, recall 88%) and the quality of the HMM tagger output trained on this noisy data (precision 78%, recall 71%). In order to generate normalized gene lists, we explored two approaches. First, we explored simple pattern matching based on synonym lists to obtain a high recall/low precision system (recall 95%, precision 2%). Using a series of filters, we were able to improve precision to 50% with a recall of 72% (balanced F-measure of 0.59). Our second approach combined the HMM gene mention tagger with various filters to remove ambiguous mentions; this approach achieved an F-measure of 0.72 (precision 88%, recall 61%). These experiments indicate that the lexical resources provided by FlyBase are complete enough to achieve high recall on the gene list task, and that normalization requires accurate disambiguation; different strategies for tagging and normalization trade off recall for precision.

Machine-learning based entity extraction requires a large corpus of annotated training to achieve acceptable results. However, the cost of expert annotation of relevant data, coupled with issues of inter-annotator variability, makes it expensive and time-consuming to create the necessary corpora. We report here on a simple method for the automatic creation of large quantities of imperfect training data for a biological entity (gene or protein) extraction system. We used resources available in the FlyBase model organism database; these resources include a curated lists of genes and the articles from which the entries were drawn, together a synonym lexicon. We applied simple pattern matching to identify gene names in the associated abstracts and filtered these entities using the list of curated entries for the article. This process created a data set that could be used to train a simple Hidden Markov Model (HMM) entity tagger. The results from the HMM tagger were comparable to those reported by other groups (F-measure of 0.75). This method has the advantage of being rapidly transferable to new domains that have similar existing resources.